mongo-datatables 1.1.2

Server-side processing for DataTables and Editor with MongoDB

mongo-datatables

Server-side processing for jQuery DataTables with MongoDB.

Overview

This package provides an elegant bridge between jQuery DataTables and MongoDB databases, handling the translation of DataTables server-side requests into optimized MongoDB queries. It supports both read operations and full CRUD functionality when paired with DataTables Editor.

Key Capabilities

• Server-side processing for efficient handling of large datasets
• Advanced search functionality with column-specific filtering
• Multi-column sorting with MongoDB optimization
• Complete Editor integration for create, read, update, and delete operations
• Framework-agnostic design compatible with Flask, Django, and other Python web frameworks

Installation

pip install mongo-datatables

Basic Implementation

from flask import request, jsonify
from mongo_datatables import DataTables

@app.route('/data/<collection>', methods=['POST'])
def get_data(collection):
    data = request.get_json()
    results = DataTables(mongo, collection, data).get_rows()
    return jsonify(results)

Documentation

For comprehensive documentation, visit mongo-datatables.readthedocs.io

Search Functionality

How Search Works in mongo-datatables

mongo-datatables provides powerful search capabilities that adapt based on your MongoDB configuration and search syntax. Understanding how search works can help you optimize performance, especially for large collections.

Search Types and Performance

Type	Example	Large Collection Perf	Description
Text	George Orwell	Fast 100-300ms	Text search with indexes (OR semantics)
Phrase	"Margaret Atwood"	Fast 100-300ms	Exact phrase matching (exact match)
Field	Author:Bradbury	Moderate 1-2s	Field-specific search (single field)
Comparison	Pages:>100	Fast 200-500ms	Numeric/date compare (>, <, >=, <=, =)
Combined	Author:"Huxley" Year:>2000	Moderate 0.5-1s	Multiple search types (AND semantics)
Regex	George Orwell (no index)	Slow 5-10s+	Fallback search (OR semantics)
Mixed	Title:"Ski" Ishiguro	Moderate 300-700ms	Phrase + text (AND between terms)

Performance metrics based on large collections (>2M docs)

Performance Optimization

Importance of Indexes for Large Collections

When working with large MongoDB collections, creating proper indexes is critical for performance. Without appropriate indexes, queries can become extremely slow or timeout entirely.

Text Indexes for Search Performance

The DataTables class is designed to leverage MongoDB text indexes for efficient search operations:

# Create a text index in MongoDB (do this once in your setup script)
db.your_collection.create_index([("field1", "text"), ("field2", "text")])

# DataTables will automatically use the text index when available
datatables = DataTables(mongo, 'your_collection', request_args)

Benefits of text indexes:

• Dramatically faster search on large collections
• Better relevance scoring for search results
• Language-aware stemming for more natural search
• Support for exact phrase queries using quotes

If a text index is not available, the library will fall back to regex-based search, which is significantly slower for large collections.

Regular Indexes for Sorting and Filtering

In addition to text indexes, create regular indexes for fields used in sorting and filtering:

# Create indexes for commonly sorted/filtered fields
db.your_collection.create_index("created_at")
db.your_collection.create_index("status")

Note: MongoDB has a limit of one text index per collection, but you can include multiple fields in a single text index.

Development

Testing

Run the tests:

python run_tests.py

Generate coverage report:

python run_coverage.py

Coverage reports are available in the htmlcov directory.

License

Released under the MIT License.